Various types of tests related to patient diagnosis and therapy can be performed by analysis of a sample of a patient's infection, bodily fluid or abscess for an analyte of interest. Patient samples are typically placed in closed sample tubes, the tubes transported to a clinical laboratory, placed into racks on an automated clinical analyzer and sample is extracted from the tubes. Subsequently, samples are combined in reaction vessels with various reagents extracted from reagent containers; the mixture is possibly incubated before being analyzed to aid in treatment of the patient. Interrogating measurements, turbidimetric or fluorometric or the like, may be employed to ascertain reaction rate values from which the amount of analyte in the sample may be determined using well-known calibration techniques. Herein, reagents, quality control, and calibration solutions may be referred to as analytical solutions.
Automated clinical analyzers improve operating efficiency by providing results more rapidly while minimizing operator or technician error. Due to increasing demands on clinical laboratories regarding assay accuracy, in particular for assays employing smaller patient samples, error sources continually need to be eliminated.
In many clinical assays, an immunochemical reaction between a soluble antigen and a bivalent or polyvalent antibody generates large groups of molecules which scatter light measurable by photometric sensors. The time profile of such reactions very frequently corresponds to the general kinetic profile of successive first order reactions from which concentration-dependent measurement signals can be determined. Such reactions are generally governed by thermodynamic factors and thus random variations in constitution or physical state of the analytical solutions employed, as well as random variations in the physical state of the analytical devices employed, may cause random variation in the measurement signals obtained. Such random variations are different from assay drift which derives from systematic changes that can be detected using well known system control procedures.
A known source of errors in clinical analyzers is the appearance of such random variations, known as outliers, in measurement data values that fall well outside established ranges or predictable limits, and therefore usually of limited value in analytical determinations.